Our primary practice is litigation support — bringing technical depth and proprietary AI to the discovery process in major litigation. We combine that with purpose-built AI products and business process reengineering to serve organizations across demanding environments.
Our core practice. We level the technical playing field in major litigation — bringing expert knowledge of enterprise records systems, discovery strategy, and Rule 26 compliance to the plaintiff's side of the table.
Purpose-built AI systems for demanding operational environments. Document intelligence, workflow automation, claimant evaluation, case management, and decision-support tools — deployed where accuracy and accountability are non-negotiable.
Technology only delivers lasting value when the processes and people around it are redesigned to use it well. We lead the organizational change — roles, workflows, governance, and culture — that makes AI adoption durable.
The most consequential decisions in major litigation are made before a single deposition is taken. We are there — with the technical depth the other side has always had.
A critical document or piece of evidence that is overlooked or hidden never comes into legal consideration. For all practical purposes, it might as well not exist.
Large corporate defendants arrive at the meet and confer table with a structural advantage. Their records are held across sophisticated enterprise systems built to manage the normal conduct of business at scale — ERP platforms, document management systems, communication archives, operational databases, and proprietary tools that have evolved over years or decades. The people who built and manage those systems understand them in detail. Defense counsel is advised by them. Plaintiff's counsel typically is not.
The asymmetry is twofold: unfamiliarity with where and how business records are created and maintained in the normal course of operations, compounded by unfamiliarity with the computing systems that hold them. Together, these gaps create a dangerous condition — one in which uninformed counsel is taken advantage of and their clients' interests are compromised before a single deposition is taken or a single motion is filed.
Under Rule 26(f), parties are required to meet and confer and develop a written discovery plan governing the scope, form, and sequence of document production. Under Rule 26(b)(1), that scope is bounded by relevance and proportionality. Both provisions are routinely used by well-advised defense counsel to narrow what enters the case. Without technical counterweight, agreements that appear reasonable on their face can silently eliminate entire categories of critical evidence.
Crivella provides that counterweight. We work alongside plaintiff's counsel at every stage of the Rule 26 process — before negotiations begin, at the table, and through the enforcement of agreements after they are made.
Data Landscape Mapping
Before negotiations begin, we build a complete picture of the opposing party's data environment — custodians, enterprise systems, databases, third-party platforms, and archived environments — so counsel knows what should be in scope before the other side defines it.
Custodian & Non-Custodial Source Intelligence
We identify who holds what — including non-custodial locations such as shared systems, operational databases, and repositories that individual custodian productions would never surface and that adversaries have no incentive to volunteer.
Spoliation Detection
We assess whether documents and data are being destroyed, migrated, or moved in ways designed to place them beyond the reach of discovery. Evidence that disappears before it is identified never comes into play.
Linguistic & Terminology Analysis
Every large organization has its own internal language — terminology, abbreviations, and operational shorthand used in the normal course of business. We analyze that language to ensure search terms and culling agreements do not silently exclude critical evidence under neutral-sounding language.
Proportionality & Scope Defense
Proportionality arguments under Rule 26(b)(1) are a standard tool for narrowing production. We ensure that scope-reduction decisions are made with full visibility into what is at stake — and that no critical document is excluded under the guise of burden reduction.
Production Sequencing & Compliance
We map the relationships between people and processes within the opposing organization and determine the optimal sequence for custodian and non-custodial productions — ensuring deposition preparation is conducted with the fullest possible knowledge. After agreements are made, we monitor compliance and identify gaps and patterns that indicate production obligations are not being honored.
Before any technology is deployed, we establish the process and governance foundation it will operate on.
Most AI projects fail in the planning stage — not because the technology isn't ready, but because organizations don't understand what they're actually asking AI to operate on. Before any implementation begins, we map the reality of how your organization works.
We examine your existing workflows, decision-making processes, data environments, and risk exposures. We identify where AI can create genuine value, where it cannot, and — critically — what needs to change in your processes before AI can be effective.
The output is a clear-eyed assessment: what's possible, what it requires, and what a responsible path forward looks like.
Workflow Mapping
Documenting how work actually gets done — not how it's supposed to get done.
Data Environment Review
Assessing the quality, structure, and governance of the data AI will rely on.
Risk & Decision Analysis
Identifying where AI influences consequential decisions and what controls need to be in place.
Readiness Report & Roadmap
A plain-language assessment with specific, prioritized recommendations for moving forward.
Process Redesign
Rebuilding workflows from the ground up around what AI makes possible — not patching the old ones.
Role & Responsibility Realignment
Defining how human judgment and AI capability divide the work — clearly and defensibly.
Change Management
Supporting the people side of adoption — training, communication, and the transition to new ways of working.
Quality & Productivity Benchmarking
Establishing the baselines against which real improvements will be measured.
Technology adoption fails when it asks people to work in new ways without redesigning how those ways actually function. We address that directly.
Process reengineering for AI adoption means more than writing new procedures. It means rethinking how decisions get made, how information flows, how work is reviewed, and how accountability is maintained when AI is part of the picture.
We work with your people to design the new operating model — one that uses AI where it creates value, keeps human judgment where it matters, and builds in the controls that make the whole thing defensible.
With the processes right, we deploy AI that is purpose-built for high-stakes environments — and built to last.
Two inventions — separated by five years — together constitute the complete architecture of what the AI industry now calls enterprise knowledge management and retrieval-augmented generation. The first, invented by Arthur Ray Crivella in 2001, built the knowledge repository: ingestion, organization, and access at scale. The second, invented in 2006, built the content identification engine: the patented methodology for scoring, curating, and bounding collections of relevant content before feeding them to AI systems. Together, they solve the fundamental problem of AI accuracy: garbage in, garbage out — at the data curation layer, before the model ever sees the input. Every major platform vendor is building toward what Crivella invented and has been applying in world-class situations for twenty-five years.
Crivella was built around a single organizing insight: in an AI-enabled organization, there are two classes of intelligent actors working with documents and data — human professionals and AI systems. Most software serves one or the other. Crivella was designed from the ground up to serve both, equally, from the same foundation.
Human users and AI systems are two sides of the same coin. Both need access to the organization's knowledge. Both need that knowledge to be organized, searchable, and contextually meaningful. Both produce outputs — work product, analyses, decisions — that feed back into the system. Crivella powers both simultaneously, from a single shared repository.
The platform ingests large volumes of documents and data produced in the normal conduct of business — any format, any source — and organizes them into a fully digital, multimedia, internet-accessible library that is linguistically trained to understand its contents. Human professionals work within it directly: searching, reviewing, assessing, producing work product. AI systems access it as a secure, structured knowledge repository, drawing on process-specific context collections to perform analysis, generation, and evaluation with the accuracy that demanding environments require.
The result is not AI-assisted work. It is not human-supervised AI. It is genuine parallel intelligence — human and machine — operating on a shared, governed, purpose-built knowledge foundation.
Ask Us About Crivella →Ingests large volumes of documents and structured data produced in normal business operations — any format, any source — and normalizes them for human and AI use.
A fully digital, multimedia, internet-accessible working environment with advanced search and retrieval — trained to understand the language and meaning of what it holds, not just keywords.
The problem with generic AI is not the model — it is what you feed it. A patented methodology (invented 2006) analyzes the corpus, applies marker sets to identify relevant content, scores each document against a relevance threshold, and iteratively refines until the output is a precisely bounded collection — containing exactly what is relevant to this task, and nothing that is not. This is what makes AI output accurate.
Serves as a secure, process-aware knowledge repository that AI systems can access directly — enabling retrieval-augmented generation grounded in your organization's actual documents and expertise.
With the process work done, implementation becomes significantly more straightforward — and significantly more likely to deliver what was promised.
We implement AI solutions built around your reengineered processes, not the other way around. That means the technology fits your actual workflows, your actual data, and the actual decisions your people need to make.
We work across document intelligence, workflow automation, decision support, large language model applications, and custom integrations — selecting and deploying what fits the problem, not what's fashionable.
Document Intelligence
AI-powered extraction, review, classification, and analysis of documents at scale.
Workflow Automation
Automating repetitive, rule-based tasks so your people can focus on the work that requires judgment.
Decision Support Systems
AI that informs and accelerates decisions — with the human oversight frameworks that keep those decisions defensible.
LLM Applications & Integration
Deploying large language model capabilities — research, drafting, summarization, Q&A — within controlled, governed environments.
Data Governance Frameworks
Policies, controls, and access structures that protect the integrity and confidentiality of the data AI uses.
AI Decision Oversight
Human-in-the-loop frameworks that ensure consequential AI outputs are reviewed, validated, and traceable.
Risk & Liability Controls
Governance structures designed to protect your organization in regulated, high-stakes, or litigation-sensitive environments.
Audit Trails & Accountability
Documentation and logging frameworks that make AI-assisted decisions defensible — to clients, regulators, and courts.
AI is only as trustworthy as the controls around it. In environments where data is sensitive, decisions are consequential, and accountability is non-negotiable — governance isn't a checkbox. It's the foundation.
We build governance frameworks that protect your data, ensure oversight of AI-influenced decisions, and manage the risk and liability exposure that AI adoption creates.
For law firms in particular, this work is essential: privilege, confidentiality, and the quality of legal advice cannot be compromised by poorly governed AI. We design the controls that make AI adoption safe in your environment.
Assessment through implementation and governance — the complete transformation engagement for organizations ready to commit to lasting change.
Scoped to a specific service area — assessment only, process reengineering only, or governance framework development — for organizations with defined, targeted needs.
Retained advisory relationships for organizations that want ongoing guidance as their AI capabilities evolve and their operational environment changes.
Every engagement begins with a conversation. Tell us where you are, where you want to go, and what's in the way. We'll take it from there.
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